1. Field of Invention
The current invention relates to automated quantification of the anatomical images of tissue structures in a human or animal subject in a non-invasive manner.
2. Discussion of Related Art
Current radiological diagnosis is based on qualitative and subjective judgment. Even though quantitative analyses may significantly improve our ability to detect and characterize abnormality, currently there are essentially no quantitative techniques accepted as a part of routine radiological diagnosis. This is partly due to difficulties in analyzing tissues based on radiological imaging such as MR and CT images. This means, even a very basic image analysis such as the brain volume, can not be readily obtained quickly and automatically.
The principal limitation of the current state of the art is that in all anatomical representation there are as many unknowns to be estimated in the atlases as there are voxels—order 10-100 million. On the other hand, known representations of human anatomy at 1 mm scale reduce its relatively homogeneous substructures.
For automated image analysis, conventional voxel-based analyses have been widely used, in which the shape of each brain is transformed to that of a template brain. Once all brains are transformed (normalized) to the template, voxel-by-voxel analyses can be performed. In this type of analysis, each voxel is treated as an independent entity and no anatomical information is used during the process.
One shortcoming of the voxel-based analysis is that the result is not reliable for the brain areas where images do not provide contrasts. In other words, if there is an area with homogeneous intensity, registration results of such area are inherently mal-imposed. However, this reliability issue can not be appreciated from the results of voxel-based analysis results readily.
Characterizing disease is an area of growing importance. Existing morphometric shape analysis has largely focused on characterizing shape abnormalities associated with a disease via voxel based morphometry. More localized approaches have advantages of discovering regions that may be affected by diseases without the prior knowledge from pathological studies. Region of interest analysis in particular brain regions, such as the hippocampus and thalamus, has been used to overcome the mis-registration and has its own interest in associating the structure change with disease stages. However, there is considerable variation in shape change across multiple structures across disease populations. The assessment of the degree and pattern of structural changes in the multiple structures in circuits is necessary to optimally distinguish subjects with early forms of various diseases. As the growth of large available databases emerge, there are tremendous demands on automatic, sensitive, and reliable methods for localizing group differences in multiple structures in neuronal circuits and identifying morphometric biomarkers associated with a specific neuropsychiatric or degenerative disease. Region of interest analysis extended to multi-structure circuits of the brain can require the ability mediate between the 10,000,000 voxels and one single structure.
Another limitation of prior art include the noisy low-level voxel-based segmentation.
Thus, there is a need in the art for reducing the complexity associated with representing homogeneous substructures of a region, for example, a tissue region.